dc.contributor.author | Jørstad, Tommy Stokmo | |
dc.contributor.author | Midelfart, Herman | |
dc.contributor.author | Bones, Atle M. | |
dc.date.accessioned | 2015-09-21T11:36:03Z | |
dc.date.accessioned | 2016-04-06T13:43:03Z | |
dc.date.available | 2015-09-21T11:36:03Z | |
dc.date.available | 2016-04-06T13:43:03Z | |
dc.date.issued | 2008 | |
dc.identifier.citation | BMC Bioinformatics 2008, 9:117 | nb_NO |
dc.identifier.issn | 1471-2105 | |
dc.identifier.uri | http://hdl.handle.net/11250/2384328 | |
dc.description.abstract | Background: Choosing the appropriate sample size is an important step in the design of a
microarray experiment, and recently methods have been proposed that estimate sample sizes for
control of the False Discovery Rate (FDR). Many of these methods require knowledge of the
distribution of effect sizes among the differentially expressed genes. If this distribution can be
determined then accurate sample size requirements can be calculated.
Results: We present a mixture model approach to estimating the distribution of effect sizes in data
from two-sample comparative studies. Specifically, we present a novel, closed form, algorithm for
estimating the noncentrality parameters in the test statistic distributions of differentially expressed
genes. We then show how our model can be used to estimate sample sizes that control the FDR
together with other statistical measures like average power or the false nondiscovery rate. Method
performance is evaluated through a comparison with existing methods for sample size estimation,
and is found to be very good.
Conclusion: A novel method for estimating the appropriate sample size for a two-sample
comparative microarray study is presented. The method is shown to perform very well when
compared to existing methods. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | BioMed Central | nb_NO |
dc.rights | Navngivelse 3.0 Norge | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/no/ | * |
dc.title | A mixture model approach to sample size estimation in two- sample comparative microarray experiments | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.date.updated | 2015-09-21T11:36:03Z | |
dc.source.volume | 9 | nb_NO |
dc.source.journal | BMC Bioinformatics | nb_NO |
dc.identifier.doi | doi:10.1186/1471-2105-9-117 | |
dc.identifier.cristin | 363755 | |
dc.description.localcode | © Jørstad et al; licensee BioMed Central Ltd. 2008. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | nb_NO |